The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

References in zbMATH (referenced in 62 articles )

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  1. Kocsis, Zoltan A.; Swan, Jerry: Genetic programming $+$ proof search $=$ automatic improvement (2018)
  2. Silberstein, Natalia; Zeh, Alexander: Anticode-based locally repairable codes with high availability (2018)
  3. Ferraro Petrillo, Umberto; Guerra, Concettina; Pizzi, Cinzia: A new distributed alignment-free approach to compare whole proteomes (2017)
  4. Fuerst, Carlo; Pacut, Maciej; Schmid, Stefan: Data locality and replica aware virtual cluster embeddings (2017)
  5. Gentle, James E.: Matrix algebra. Theory, computations and applications in statistics (2017)
  6. Gong, Xueqing; Sung, Chi Wan: Zigzag decodable codes: linear-time erasure codes with applications to data storage (2017)
  7. Lanza, Daniel; Chávez, F.; Fernandez, Francisco; Garcia-Valdez, M.; Trujillo, Leonardo; Olague, Gustavo: Profiting from several recommendation algorithms using a scalable approach (2017)
  8. Lefticaru, Raluca; Macías-Ramos, Luis F.; Niculescu, Ionuţ Mihai; Mierlă, Laurenţiu: Agent-based simulation of kernel P systems with division rules using FLAME (2017)
  9. Luo, Taibo; Zhu, Yuqing; Wu, Weili; Xu, Yinfeng; Du, Ding-Zhu: Online makespan minimization in MapReduce-like systems with complex reduce tasks (2017)
  10. Vasiliu, Laura; Pop, Florin; Negru, Catalin; Mocanu, Mariana; Cristea, Valentin; Kolodziej, Joanna: A hybrid scheduler for many task computing in big data systems (2017)
  11. Bermanis, Amit; Salhov, Moshe; Wolf, Guy; Averbuch, Amir: Measure-based diffusion grid construction and high-dimensional data discretization (2016)
  12. Choi, Woohyuk; Hong, Sumin; Jeong, Won-Ki: Vispark: GPU-accelerated distributed visual computing using Spark (2016)
  13. Derbeko, Philip; Dolev, Shlomi; Gudes, Ehud; Sharma, Shantanu: Security and privacy aspects in MapReduce on clouds: a survey (2016)
  14. Iwen, M.A.; Ong, B.W.: A distributed and incremental SVD algorithm for agglomerative data analysis on large networks (2016)
  15. Rizk, Amr; Poloczek, Felix; Ciucu, Florin: Stochastic bounds in Fork-Join queueing systems under full and partial mapping (2016)
  16. Steele, Brian; Chandler, John; Reddy, Swarna: Algorithms for data science (2016)
  17. Stewart, Robert; Maier, Patrick; Trinder, Phil: Transparent fault tolerance for scalable functional computation (2016)
  18. Yang, Chao-Tung; Shih, Wen-Chung; Huang, Chih-Lin; Jiang, Fuu-Cheng; Chu, William Cheng-Chung: On construction of a distributed data storage system in cloud (2016) ioport
  19. Zhao, Jiaqi; Tao, Jie; Streit, Achim: Enabling collaborative MapReduce on the cloud with a single-sign-on mechanism (2016) ioport
  20. Berlińska, Joanna; Drozdowski, Maciej: Scheduling multilayer divisible computations (2015)

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